RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report

• URL: http://arxiv.org/abs/2506.01947v1
• Date: Mon, 02 Jun 2025 17:58:31 GMT
• Title: RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report
• Authors: Marcos V. Conde, Radu Timofte, Radu Berdan, Beril Besbinar, Daisuke Iso, Pengzhou Ji, Xiong Dun, Zeying Fan, Chen Wu, Zhansheng Wang, Pengbo Zhang, Jiazi Huang, Qinglin Liu, Wei Yu, Shengping Zhang, Xiangyang Ji, Kyungsik Kim, Minkyung Kim, Hwalmin Lee, Hekun Ma, Huan Zheng, Yanyan Wei, Zhao Zhang, Jing Fang, Meilin Gao, Xiang Yu, Shangbin Xie, Mengyuan Sun, Huanjing Yue, Jingyu Yang Huize Cheng, Shaomeng Zhang, Zhaoyang Zhang, Haoxiang Liang,
• Abstract summary: This paper covers the second challenge on RAW Reconstruction from sRGB (Reverse ISP)<n>We aim to recover RAW sensor images from smartphones given the corresponding sRGB images without metadata.
• Score: 80.64928431399075
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Numerous low-level vision tasks operate in the RAW domain due to its linear properties, bit depth, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public sRGB datasets. For this reason, many approaches try to generate realistic RAW images using sensor information and sRGB images. This paper covers the second challenge on RAW Reconstruction from sRGB (Reverse ISP). We aim to recover RAW sensor images from smartphones given the corresponding sRGB images without metadata and, by doing this, ``reverse" the ISP transformation. Over 150 participants joined this NTIRE 2025 challenge and submitted efficient models. The proposed methods and benchmark establish the state-of-the-art for generating realistic RAW data.

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